Abstract
There is a tension in the world between complexity and simplicity. On one hand, we are faced with a richness of environment and experience that is at times overwhelming. On the other, we seem to be able to cope and even thrive within this complexity through the use of simple scripts, stereotypical judgements, and habitual behaviors. In order to function in the world, we have idealized and simplified it in a way that makes reasoning about it more tractable. As a group and as individuals, human agents search for and create islands of simplicity and stability within a sea of complexity and change.
In this article, we will discuss an approach based on the case-based reasoning paradigm that attempts to resolve this tension. This agency approach embraces, rather than avoids, this paradox of the apparent complexity of the world and the overall simplicity of our methods for dealing with it. It accomplishes this by treating the behavior of intelligent agents as an ongoing attempt to discover, create, and maintain the stability that is necessary for the production of actions that satisfy our goals.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Alterman, R. (1986). An adaptive planner. In Proceedings of the Fifth NationalConference on Artificial Intelligence (pp. 65–69). Philadelphia, PA: AAAI.
Bareiss, R. (1989). Exemplar-based knowledge acquisition. Vol. 2 of Perspective in artificial intelligence.San Diego, CA: Academic Press.
Birnbaum, L., amp; Collins, G. (1984). Opportunistic planning and Freudian slips. In Proceedings of the Sixth Annual Conference of the Cognitive Science Society,Boulder, CO.
Birnbaum, L.A. (1986). Integrated processing in planning and understanding(Technical Report 489). Ph.D. thesis, Yale University.
Byrne, R. (1977). Planning meals: Problem solving on a real data base. Cognition, 5.
Carbonell, J. (1981). Counterplanning: A strategy-based model of adversary planning inreal-world situations.Artificial Intelligence, 16, 295–329.
Chapman, D. (1985). Nonlinear planning: A rigorous reconstruction. In Proceedings of the Ninth International Joint Conference on Artificial Intelligence(pp. 1022–1024). IJCAI.
Dean, T., Firby, R.J., amp; Miller, D. (1987). The forbin paper (TechnicalReport 550). Department of Computer Science, Yale University.
Fikes, R., amp; Nilsson, N. (1971). STRIPS: A new approach to the application oftheorem proving to problem solving. Artificial Intelligence, 2, 189–208.
Firby, R.J. (1989). Adaptive execution in complex dynamic worlds (ResearchReport 672). Computer Science Department, Yale University.
Gentner, D. amp; Landers, R. (1985). Analogical reminding: A good match is hard tofind. In Proceedings of the International Conference on Systems, Man andCybernetics, Tuscon, AZ, November.
Hammond, K.J., Converse, T., amp; Marks, M. (1988). Learning from opportunities:Storing and reusing executiontime optimizations. In The Proceedings of the SeventhNational Conference on Artificial Intelligence (pp. 536–540). AAAI.
Hammond, K.J., Converse, T., amp; Marks, M. (1989). Learning from opportunity.In Proceedings of the Sixth International Workshop on Machine Learning.Ithaca, NY: Morgan Kaufmann.
Hammond, K.J., Converse, T., amp; Martin, C. (1990). Integrating planning andacting in a case-based framework.In The Proceedings of the 1990 National Conference of Artificial Intelligence,August.
Hammond, K. (1986). Case-based planning: An integrated theory of planning,learning and memory (Technical Report 488). Ph.D. thesis, Yale University.
Hammond, K. (1986). Chef: A model of case-based planning. In Proceedings ofthe Fifth National Conference on Artificial Intelligence. Philadelphia, PA: AAAI.
Hammond, K. (1986). Learning to anticipate and avoid planning problems through theexplanation of failures.In The Proceedings of the Fifth National Conference on Artificial Intelligence.Philadelphia, PA: AAAI.
Hammond, K. (1989). Case-based planning: Viewing planning as a memorytask. Vol. 1 of Perspectives in artificial intelligence. San Diego, CA: Academic Press.
Hammond, K.J. (1990). Case-based planning: A framework for planning fromexperience. The Journal of Cognitive Science, Fall 1990. Norwood, NJ: Ablex Publishing.
Hammond, K.J. (1990). Explaining and repairing plans that fail. ArtificialIntelligence Journal, 45 (2).
Hammond, K.J. (1990). Learning and enforcement: Stabilizing environments to fecilitateactivity. In The Proceedings of the Seventh International Conference on MachineLearning, July.
Hammond, K.J. (1991). Learning and enforcement: Stabilizing environments to fecilitateactivity. In aaai91, August.
Hastie, R., amp; Kumar, A. (1979). Person memory: Personality traits as organizingprinciples in memory for behaviors. Journal of Personality and Social Psychology,37.
Hayes-Roth, B., amp; Hayes-Roth, F. (1979). A cognitive model of planning. Cognitive Science, 3 (4), 275–310.
Johnson, H.K., amp; Seifert, C.M. (1991). Predictive features in analogical access.Journal of Memory and Language.
Kolodner, J.L., amp; Simpson, R.L. (1989). The mediator: Analysis of an early case-based problem. Cognitive Science Journal.
Kolodner, J. (1984). Retrieval and organizational strategies in conceptualmemory. Hillsdale, NJ: Lawrence Erlbaum Associates.
Kristian, M.M., Hammond, J., amp; Converse, T. (1989). Planning in an open world:A pluralistic approach. In Proceedings fo the 1989 Meeting of the Cognitive Science Society, AnnArbor, MI. Hillsdale, NJ: Lawrence Erlbaum Associates.
Lebowitz, M. (1980). Generalization and memory in an integrated understandingsystem (Technical Report 186). Ph.D. thesis, Yale University.
Lesser, V., Fennell, R., Erman, L., amp; Reddy, D. (1975). Organization of theHearsay-II speech understanding system. IEEE Transactions on Acoustics, Speech,Signal Processing, ASSP, 23, 11–33.
Martin, C.E. (1990). Direct memory access parsing. Ph.D. thesis, Yale University.
McDermott, D. (1978). Planning and acting. Cognitive Science, 2,71–109.
Newell, A., amp; Simon, H. (1972). Human problem solving. EnglewoodCliffs, NJ: Prentice-Hall.
Owens, C. (1990). Indexing and retrieving abstract planning knowledge. Ph.D.thesis, Department of Computer Science, Yale University. In preparation.
Prentice, W. (1944). The interruption of tasks. Psychological Review, 51.
Robinson, S., amp; Kolodner, J. (1991). Indexing cases for planning and acting indynamic environments: Exploiting hierarchical goal structures. In Proceedings of the13th Annual Conference of the Cognitive Science Society. Chicago, IL: CognitiveScience Society.
Sacerdoti, E. (1975). A structure for plans and behavior (Technical Report109). SRI Artificial Intelligence Center.
Schank, R.C., amp; Abelson, R. (1977). Scripts, plans, goals and understanding:An inquiry into human knowledge structures. Hillsdale, NJ: Lawrence ErlbaumAssociates.
Schank, R. (1982). Dynamic memory: A theory of reminding and learning incomputers and people. Cambridge: Cambridge University Press.
Seifert, C.M., amp; Patalano, A.J. (1991). Memory for interrupted tasks: TheZeigarnik effect revisited. In Proceedings of the Thirteenth Annual CognitiveScience Society. Hillsdale, NJ: Lawrence Erlbaum Associates.
Stefik, M. (1981). Planning with constraints. Artificial Intelligence, 16,141–169.
Zeigarnik, B. (1927). Das behalten erledigter und unerledigter handlungen.Psychologische Forschungen, 91, 1–85.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Hammond, K., Converse, T., Marks, M. et al. Opportunism and Learning. Machine Learning 10, 279–309 (1993). https://doi.org/10.1023/A:1022639127361
Issue Date:
DOI: https://doi.org/10.1023/A:1022639127361